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SVLAT: Scientific Visualization Literacy Assessment Test

Patrick Phuoc Do, Kaiyuan Tang, Kuangshi Ai, Chaoli Wang

Abstract

Scientific visualization (SciVis) has become an essential means for exploring, understanding, and communicating complex scientific phenomena. However, the field still lacks a validated instrument assessing how well people read, understand, and interpret them. We present a scientific visualization literacy assessment test (SVLAT) that measures the general public's SciVis literacy. Covering a range of visualization forms and interpretation demands, SVLAT comprises 49 items grounded in 18 scientific visualizations and illustrations spanning eight visualization techniques and 11 tasks. Instrument development followed a staged, psychometrically grounded pipeline. We defined the construct and blueprint, followed by item generation, and expert review with five SciVis experts using the content validity ratio (mean CVR = 0.79). We subsequently administered a pilot test (30 participants) and a large-scale test tryout (485 participants) to evaluate the instrument's psychometric properties. For validation, we performed item analysis and refinement using both classical test theory (CTT) and item response theory (IRT) to examine item functioning and overall test quality. SVLAT demonstrates high reliability in the tryout sample (McDonald's omega_t = 0.82, Cronbach's alpha = 0.81). The assessment materials are available at https://osf.io/hr3nw/.

SVLAT: Scientific Visualization Literacy Assessment Test

Abstract

Scientific visualization (SciVis) has become an essential means for exploring, understanding, and communicating complex scientific phenomena. However, the field still lacks a validated instrument assessing how well people read, understand, and interpret them. We present a scientific visualization literacy assessment test (SVLAT) that measures the general public's SciVis literacy. Covering a range of visualization forms and interpretation demands, SVLAT comprises 49 items grounded in 18 scientific visualizations and illustrations spanning eight visualization techniques and 11 tasks. Instrument development followed a staged, psychometrically grounded pipeline. We defined the construct and blueprint, followed by item generation, and expert review with five SciVis experts using the content validity ratio (mean CVR = 0.79). We subsequently administered a pilot test (30 participants) and a large-scale test tryout (485 participants) to evaluate the instrument's psychometric properties. For validation, we performed item analysis and refinement using both classical test theory (CTT) and item response theory (IRT) to examine item functioning and overall test quality. SVLAT demonstrates high reliability in the tryout sample (McDonald's omega_t = 0.82, Cronbach's alpha = 0.81). The assessment materials are available at https://osf.io/hr3nw/.
Paper Structure (29 sections, 1 equation, 10 figures, 3 tables)

This paper contains 29 sections, 1 equation, 10 figures, 3 tables.

Figures (10)

  • Figure 1: Observed distribution of visualization techniques in a corpus of public-facing scientific visual materials across multiple outlets.
  • Figure 1: CTT item difficulty and discrimination for the 51 SVLAT tryout items. Items 49 and 69 are removed from the final SVLAT version.
  • Figure 2: The 19 visualizations considered during SVLAT development. Indices marked with * indicate animations; all others are static images. Visualization 17 is excluded based on the expert review results.
  • Figure 2: Posterior median Bayesian IRT item easiness and discrimination for the 51 SVLAT tryout items. Items 49 and 69 are removed from the final SVLAT version.
  • Figure 3: IRT item parameter summary for the 51 SVLAT tryout items. Each dot shows the median easiness/discrimination estimate, with the lighter bars indicating the 95% credible intervals and the darker bars indicating the 66% credible intervals.
  • ...and 5 more figures